Modern data centers invariably face performance challenges due to the rising volume of datasets and complexity of deep learning workloads. Sizeable research and development has taken place to understand AI/ML workloads. These workloads are computationally intensive, but also require vast amounts of data to train models and draw inferences. The impact of storage on AI/ML pipelines therefore merits additional study.